One country, two crises: what Covid-19 reveals about health inequalities among BAME communities in the United Kingdom and the sustainability of its health system?
Bibliographic record
Abstract
There has been mounting evidence of the disproportionate involvement of black, Asian and minority ethnic (BAME) communities by the Covid-19 pandemic. In the UK, this racial disparity was brought to the fore by the fact that the first 11 doctors to die in the UK from Covid-19 were of BAME background. The mortality rate from Covid-19 among people of black African descent in English hospitals has been shown to be 3.5 times higher when compared to rates among white British people. A Public Health England report revealed that Covid-19 was more likely to be diagnosed among black ethnic groups compared to white ethnic groups with the highest mortality occurring among BAME persons and persons living in the more deprived areas. People of BAME background account for 4.5% of the English population and make up 21% of the National Health Service (NHS) workforce. The UK poverty rate among BAME populations is twice as high as for white groups. Also, people of BAME backgrounds are more likely to be engaged in frontline roles. The disproportionate involvement of BAME communities by Covid-19 in the UK illuminates perennial inequalities within the society and reaffirms the strong association between ethnicity, race, socio-economic status and health outcomes. Potential reasons for the observed differences include the overrepresentation of BAME persons in frontline roles, unequal distribution of socio-economic resources, disproportionate risks to BAME staff within the NHS workspace and high ethnic predisposition to certain diseases which have been linked to poorer outcomes with Covid-19. The ethnoracialised differences in health outcomes from Covid-19 in the UK require urgent remedial measures. We provide intersectional approaches to tackle the complex racial disparities which though not entirely new in itself, have been often systematically ignored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".